Stochastic Expectation Propagation Learning of Infinite Multivariate Beta Mixture Models for Human Tissue Analysis
Narges Manouchehri, Nizar Bouguila · 2021
Nowadays, there is considerable and growing interest in applying accurate analysis tools to obtain meaningful information and extract knowledge from a huge amount of data. In this sense, unsupervised algorithms and clustering techniques have gained an increasing interest. These methods are helpful specifically when data annotation is time-consuming and costly. In this paper, we propose a new clustering method based on a Dirichlet process mixture of multivariate Beta distributions. To learn this novel Bayesian nonparametric model, we applied stochastic expectation propagation inference framework. This framework is able to define the model complexity and estimate the model’s parameters simultaneously. To demonstrate the efficiency of our model, we perform an experimental analysis using three real applications, breast, lung and colon histopathological tissue analysis. Our goal is to show that our algorithm could be considered as a machine learning framework in computer-assisted diagnosis and play the role of a complementary opinion to help the pathologists in making decisions with more accuracy.